There are many well known methods for collecting vibration data for performing predictive maintenance. Typically, in these methods a vibration sensor such as a piezoelectric accelerometer is mechanically coupled to the machine to be monitored. The vibration sensor collects vibrations from the machine and converts these vibration to an electrical signal. The electrical signal is processed by suitable signal processing and converted from analog to digital form. The resulting digital signal is stored for later analysis.
Analysis of a vibration signal from a machine typically involves one or both of (1) comparing that signal to previously collected signals to identify an variations that would be indicative of machine wear and possible impending failure, and (2) forming a frequency spectrum of the vibration signal and evaluating this spectrum for patterns indicative of potential failures. Typically these operations are performed through the use of a data collector. A data collector includes analog signal processing electronics for receiving a vibration signal and conditioning that signal, and an analog to digital converter for digitally sampling the analog signal so that it may be stored and analyzed. Signal analysis may be performed using the data collector itself or by uploading collected and digitized signals from the data collector to a host computer where signal analysis is performed.
Typically the analog front end of a data collector includes a number of selectable analog signal conditioning circuits, each selectable by controlling an analog switch for connecting the element into the signal path through the data collector. As seen in FIG. 1, a typical data collector might include a first signal conditioning section in which analog switches 12 and 22 may be used to selectively pass the incoming analog signal through a high pass filter 14 or other analog signal processing blocks 14′ and 14″ (these blocks may include, e.g., low pass or band pass filters). Furthermore, a second signal conditioning section in the data collector might include additional analog switches 24 and 31 for selectively connecting the incoming vibration signal through a peak detecting circuit 30 or another analog signal processing circuit 30′. After these processing blocks, the signal may be further conditioned, and low pass filtered, and then converted from analog to digital form by an analog to digital converter, and stored in a digital memory.
As an example of the kinds of analysis performed with a data collector, consider a rotating machine that generates vibration signals at a fundamental frequency that corresponds to the frequency of rotation of the machine. During normal operation, additional vibration signals will be generated at higher frequencies as well. These higher frequency vibrations correspond to interactions of mechanical parts while the machine rotates, such as movements of moving parts in bearings, sliding pistons and cams, resonances of machine components, and other normal mechanical activity attendant to rotation of the machine. If, however, a bearing or other mechanical system begins to fail, the part will begin to generate different frequency patterns. For example, a bearing may develop a crack, causing a “click” to occur each time weight is applied to the crack in the bearing, which will be reflected as increased higher frequency vibration in a vibration signal from the machine. Alternatively, a sliding mechanical part may begin to fail and scrape undesirably as it moves, again causing increased higher frequency vibration.
It will be noted that collection and analysis of high frequency energy in a vibration signal is often critical in predictive maintenance analysis. For this reason, various techniques have been developed for isolating high frequency pulses or other high frequency information in a vibration signal. Typically, these methods further involve generating a lower frequency signal that quantifies the high frequency energy in the original signal, so that the lower frequency signal may be digitized and analyzed. Several of these techniques will be reviewed below.
So-called “shock pulse” analysis, developed by SPM Instrument AB of Sweden in the 1970's, uses a special transducer having a tuned resonant frequency at 32 kHz. Thus, this transducer is most sensitive to “shock pulse” signals in this frequency band, which are often indicative of bearing defects and poor lubrication. The output of the resonant transducer is reflective of energy in the frequency band of the transducer, and is used to develop a measure on a scale of 1 to 100 of the high frequency energy in the signal, with a value near 100 indicating a failure mode.
The Kurtosis method, developed by British Steel and the University of Southampton in the 1970's, applies a statistical method to isolated frequency bands, to develop a statistical parameter indicating the distribution of energy of the vibration signal in these various frequency bands.
This analysis is typically performed on bands from 2.5-5.0 kHz, 5-10 kHz, 10-20 kHz, 20-40 kHz, 40-80 kHz, and a sum is generated of the Kurtosis parameters for each of the five bands to produce an overall measure of the high frequency content in the signal.
An enveloping process has been used by various predictive maintenance companies including Computational Systems Incorporated, SKF, and Diagnostics Instruments. In this process, the vibration signal is rectified and low-pass filtered, which has the effect of demodulating high frequency energy in the signal to base band; the amplitude of the resulting signal is reflective of high frequency energy in the signal prior to demodulation.
A final method, known as “spike energy detection” or alternatively “Peak Vue”, has been used by the assignee of the present application as well as others to generate a measure of the high frequency energy in a vibration signal. FIG. 1 illustrates the typical analog circuit components that would be used for performing spike energy detection. In FIG. 1, the data collector has been configured for spike energy detection; thus, the electrical signal received from the vibration sensor is routed through analog switch 12 to high pass filter 14. As can be seen in FIG. 1, in a typical situation where the spike energy method would be useful, the transducer signal would include a low frequency vibration signal (seen in FIG. 1 as a sinusoidal waveform 16), superimposed with brief spikes of high frequency vibration 18. High pass filter 14 removes the low frequency sinusoidal waveform from the incoming vibration signal, and passes the higher frequency spikes 18, resulting in a signal as seen in FIG. 1 where the spikes 18 are superimposed upon a flat low frequency baseline signal 20.
The output of high pass filter 14, after passing through a second analog switch 22, is delivered through analog switch 24 to a decayed peak-to-peak detector 30. Detector 30 outputs a signal reflecting the peak-to-peak amplitude of the signal received at its input. This function is achieved by a combination of resistors and capacitors with op-amp circuits forming near-ideal diodes.
Specifically, a first portion of the detector 30 comprises capacitor C1, resistor R1 and an op-amp circuit that behaves as a near-ideal diode and is therefore illustrated as a diode D1. Through the action of diode D1, upon each negative voltage swing of the signal delivered to detector 30, capacitor C1 will accumulate a sufficient charge to have a peak voltage equal to the negative peak amplitude of the input signal. This charge will discharge from capacitor C1 through resistor R1 whenever the input signal is above its peak negative amplitude. The rate of discharge is determined by the time constant formed by multiplying R1 times C1. Typically, this time constant is chosen to approximate the reciprocal of the cutoff frequency of the high pass filter 14. As a consequence, only those frequencies of interest above the cutoff frequency will pass through capacitor C1 and be delivered to the second portion of detector 30.
The second portion of detector 30 comprises a second op-amp circuit that behaves as a buffer and near-ideal diode, and accordingly is illustrated as a buffer B1 and diode D2. The output of this op-amp circuit is delivered to a parallel connection of a capacitor C2 and resistor R2. Due to the presence of diode D2, capacitor C2 will he charged to the voltage across resistor R1 and diode D1 whenever that voltage is greater than the voltage currently across capacitor C2. Thus, capacitor C2 charges to a value representative of the peak-to-peak value of the input signal, comprised of the sum of the capacitor C1 voltage produced by the first portion of detector 30 and the positive peak amplitude of the input signal. Capacitor C2 discharges charge accumulated in this manner through resistor R2, at a rate determined by the time constant formed by multiplying R2 times C2. This time constant is normally chosen to be proportional to the period of repetition of the spikes in the input signal.
As a consequence of the long time constant R2C2, the waveform output from detector 30 has a sawtooth-like waveform 32 with a substantial low frequency component. This waveform call be readily digitized, and compared with previously recorded vibration signals and/or frequency transformed for analysis, as described above.
A difficulty inherent in the various high frequency energy detection methods described above is their lack of flexibility. In each of the above-described methods, specified frequency bands of the incoming vibration signal are isolated using special purpose analog circuitry. It different applications or different machines require the use of different frequency bands, redundant analog circuitry would need to be included in the data collector; i.e., the data collector would need to have multiple high pass filters and multiple peak-to-peak detectors, one for each frequency band of interest. Alternatively, the filters and detectors in the data collector may be made adjustable, but this would also require complexity, namely, in the filters and detectors, analog switches would need to be included to select between circuit components of different values, in order to change the frequency bands and time constants of the circuit. This approach may also encounter problems with noise due to the number of switches that are included in the analog signal path.
Accordingly, there is a need for a data collector and data collection method that is suitable for detection of high frequency energy in a signal, facilitating use in a wide range of applications.